{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Matplotlib 柱状图\n",
    "### 创建柱状图\n",
    "使用 bar() 函数来创建竖直柱状图\n",
    "使用 barh() 函数来创建水平柱状图"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 竖直柱状图\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.array(['A', 'B', 'C', 'D'])\n",
    "y = np.array([100, 20, 57, 89])\n",
    "\n",
    "plt.bar(x, y)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 水平柱状图\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.array(['A', 'B', 'C', 'D'])\n",
    "y = np.array([100, 20, 57, 89])\n",
    "\n",
    "plt.barh(x, y)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 柱状条颜色\n",
    "bar() 和 barh() 可用带入关键字参数 color 来设置柱状条的颜色"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建自定义颜色柱状图\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.array(['A', 'B', 'C', 'D'])\n",
    "y = np.array([100, 20, 57, 89])\n",
    "\n",
    "plt.bar(x, y, color='y')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 柱状条宽度\n",
    "bar() 带入关键字参数 width 来设置柱状条的宽度\n",
    "默认宽度值为 0.8"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 指定柱状条宽度\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.array(['A', 'B', 'C', 'D'])\n",
    "y = np.array([100, 20, 57, 89])\n",
    "\n",
    "plt.bar(x, y, color='y', width=0.4)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 柱状条高度\n",
    "barh() 带入关键字参数 height 来设置柱状条的高度\n",
    "默认高度是 0.8"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 指定柱状条高度\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.array(['A', 'B', 'C', 'D'])\n",
    "y = np.array([100, 20, 57, 89])\n",
    "\n",
    "plt.barh(x, y, height=0.4)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}